Men’s Awareness on Sexism Issues Experienced by Women Portrayed in Éléonore Pourriat’s Oppressed Majority : A Reader Response Study
Bibliographic record
Abstract
This research entitled “Men’s Awareness on Sexism Issues Experienced by Women Portrayed in Eleonore Pourriat’s Oppressed Majority: A Reader Response Study” is aimed to figuring out the awareness of male respondents from ten different countries toward sexism issues experienced by women as depicted in Eleonore Pourriat’s Oppressed Majority short film. Moreover, this research elaborates the supported aspects of sexism awareness or unawareness from the respondents. Furthermore, the researcher used qualitative methods since the research deals with people’s opinion and experience in order to understand the issue of sexism which is often related to women violence and dependency. The primary data of this research are the responses obtained from respondents related to sexism issues experienced by women. Consequently, the result of this research reveals both respondents who are aware and unaware. The researcher finds awareness of the respondents from two aspects which are supported by environment and self-aware. Respondent from Austria, Argentina, United Kingdom, and United States of America are categorized as being aware supported by environment. Besides, respondent from Canada, France, Netherland, and Indonesia are categorized as being self-awareness. On the contrary, the unawareness aspects are being ignorant, lack of knowledge and being unaware on benevolent sexism. Respondent from Albania is the one who unaware because of his ignorance. Indian respondent is unaware because of his lack of knowledge. Then, the Dutch respondent is also categorized as being unaware of benevolent sexism. Finally, this research proves the existence of sexism in everyday life experienced by women and the importance of surrounding on raising the sexism awareness for people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".